更新时间：2021-06-11 13:46:13
封面
版权页
Preface
About the Book
Chapter 1 Data Preparation and Cleaning
Introduction
Data Models and Structured Data
pandas
Data Manipulation
Summary
Chapter 2 Data Exploration and Visualization
Identifying the Right Attributes
Generating Targeted Insights
Visualizing Data
Chapter 3 Unsupervised Learning: Customer Segmentation
Customer Segmentation Methods
Similarity and Data Standardization
k-means Clustering
Chapter 4 Choosing the Best Segmentation Approach
Choosing the Number of Clusters
Different Methods of Clustering
Evaluating Clustering
Chapter 5 Predicting Customer Revenue Using Linear Regression
Understanding Regression
Feature Engineering for Regression
Performing and Interpreting Linear Regression
Chapter 6 Other Regression Techniques and Tools for Evaluation
Evaluating the Accuracy of a Regression Model
Using Regularization for Feature Selection
Tree-Based Regression Models
Chapter 7 Supervised Learning: Predicting Customer Churn
Classification Problems
Understanding Logistic Regression
Creating a Data Science Pipeline
Modeling the Data
Chapter 8 Fine-Tuning Classification Algorithms
Support Vector Machines
Decision Trees
Random Forest
Preprocessing Data for Machine Learning Models
Model Evaluation
Performance Metrics
Chapter 9 Modeling Customer Choice
Understanding Multiclass Classification
Class Imbalanced Data
Appendix
Chapter 1: Data Preparation and Cleaning
Chapter 2: Data Exploration and Visualization
Chapter 3: Unsupervised Learning: Customer Segmentation
Chapter 4: Choosing the Best Segmentation Approach
Chapter 5: Predicting Customer Revenue Using Linear Regression
Chapter 6: Other Regression Techniques and Tools for Evaluation
Chapter 7: Supervised Learning: Predicting Customer Churn
Chapter 8: Fine-Tuning Classification Algorithms
Chapter 9: Modeling Customer Choice
